code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if len(__UpperCAmelCase ) == 0:
return []
UpperCAmelCase_, UpperCAmelCase_ : int = min(__UpperCAmelCase ), max(__UpperCAmelCase )
UpperCAmelCase_ : in... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenizati... | 31 | 0 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, requir... | 270 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 31 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 591 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2... | 31 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}
try:
... | 417 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFea... | 31 | 0 |
import math
import qiskit
def a_ (__A = 1 , __A = 1 , __A = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(__UpperCAmelCase , __UpperCAmelCase )
or isinstance(__UpperCAmelCase , __UpperC... | 351 |
from __future__ import annotations
from typing import TypedDict
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list[str]:... | 31 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
fro... | 161 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str ):
SCREAMING_SNAKE_CASE_ = {}
def lowerCAmelCase_ ( self : List[str] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCAmelCase , ... | 31 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vis... | 650 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
... | 31 | 0 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCamelCase_( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 661 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : List[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int=None , UpperCamelC... | 77 |
def UpperCAmelCase_ ( __UpperCAmelCase : list , __UpperCAmelCase : int , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 ) -> int:
SCREAMING_SNAKE_CASE_ = right or len(__UpperCAmelCase ) - 1
if left > right:
... | 31 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a ( _SCREAM... | 202 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 31 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStr... | 325 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase_ ( ) -> Generator[int, None, None]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 2
while True:
SCREAMING_SNAKE_CASE_ = factor_map.pop(__Uppe... | 31 | 0 |
def lowerCamelCase__ ( _lowercase = 4000000 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = []
UpperCAmelCase_, UpperCAmelCase_ : int = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCAmelCase )
UpperCAmelCa... | 30 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 0 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable... | 270 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( se... | 31 | 0 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors impor... | 591 |
def UpperCAmelCase_ ( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowerCamelCase__ : List[Any] = generate_large_matrix()
lowerCamelCase__ : List[Any] = (
[[4, 3, 2, -1], [3,... | 31 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a_ = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'... | 417 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Optional[int] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 31 | 0 |
import math
def a_ (__A , __A ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
... | 351 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCamelCase_ :
'''simple docstring'''
@property
def lowerCAmelCase_ ... | 31 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : int = 1_0 , lowercase : int = 1_0_0_0 , lowercase : bool = True ) ->int:
"""simple docstring"""
assert (
isinstance(__UpperCAmelCase , __UpperCAmelCase )
and isinsta... | 161 |
import operator as op
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNA... | 31 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[Any] ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autof... | 650 |
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE_ = ... | 31 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Li... | 661 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 31 | 0 |
"""simple docstring"""
from functools import reduce
A = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'668966489... | 77 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_torch
def lowerCAmelCase_ ( self ... | 31 | 0 |
def lowerCAmelCase ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
return number | (1 << position)
def lowerCAmelCase ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simpl... | 202 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "M-CLIP"
def __init__( self : Tuple , _lowerCAmelCase : List[st... | 31 | 0 |
'''simple docstring'''
def _A ( lowercase__ = 10 , lowercase__ = 22 ):
lowercase__ = range(1 , __UpperCAmelCase )
lowercase__ = range(1 , __UpperCAmelCase )
return sum(
1 for power in powers for base in bases if len(str(base**power ... | 325 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pi... | 31 | 0 |
from pathlib import Path
import fire
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = Path(__UpperCAmelCase )
UpperCAmelCase_ : str = Path(__UpperCAmelCase )
dest_dir.mkdir(e... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenizati... | 31 | 0 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowercase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
A... | 270 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 31 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCamelCase__ : Any = 100
UpperCamelCase__ : Optional[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCamelCase__ : int
for prime in range(3, ceil(NUM_PRIMES**0.... | 591 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2... | 31 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __lowercase ( lowerCamelCase : str ): # picklable for multiprocessing
return i + 1
... | 417 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFea... | 31 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def a_ (__A=None , __A=None ) -> str:
"""sim... | 351 |
from __future__ import annotations
from typing import TypedDict
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list[str]:... | 31 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
... | 161 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str ):
SCREAMING_SNAKE_CASE_ = {}
def lowerCAmelCase_ ( self : List[str] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCAmelCase , ... | 31 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 200 )-> int:
_lowerCamelCase = [1, 2, 5, 10, 20, 50, 100, 200]
_lowerCamelCase = [0] * (pence + 1)
_lowerCamelCase = 1 # base case: 1 way to make 0 pence
... | 650 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
... | 31 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
... | 661 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
clas... | 77 |
def UpperCAmelCase_ ( __UpperCAmelCase : list , __UpperCAmelCase : int , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 ) -> int:
SCREAMING_SNAKE_CASE_ = right or len(__UpperCAmelCase ) - 1
if left > right:
... | 31 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , *_lowerCAmelCase , **_lowerCAm... | 202 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 31 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
... | 325 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase_ ( ) -> Generator[int, None, None]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 2
while True:
SCREAMING_SNAKE_CASE_ = factor_map.pop(__Uppe... | 31 | 0 |
class __a: # Public class to implement a graph
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[int]:
UpperCAmelCase_ : Any = row
UpperCAmelCase_ : List[str] = ... | 30 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common i... | 270 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( se... | 31 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamel... | 591 |
def UpperCAmelCase_ ( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowerCamelCase__ : List[Any] = generate_large_matrix()
lowerCamelCase__ : List[Any] = (
[[4, 3, 2, -1], [3,... | 31 | 0 |
def __lowercase ( lowerCamelCase : list[list[int | float]] ):
UpperCamelCase_ : Optional[Any] = len(__UpperCAmelCase )
UpperCamelCase_ : Dict = len(matrix[0] )
UpperCamelCase_ : List[Any] = min(__UpperCAmelCase , __UpperCAmelCase )
for row in range(__UpperC... | 417 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Optional[int] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 31 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = '▁'
UpperCAm... | 351 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCamelCase_ :
'''simple docstring'''
@property
def lowerCAmelCase_ ... | 31 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : list , lowercase : int , lowercase : int = 0 , lowercase : int = 0 ) ->int:
"""simple docstring"""
lowercase__ = right or len(__UpperCAmelCase ) - 1
if ... | 161 |
import operator as op
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNA... | 31 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 650 |
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE_ = ... | 31 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 661 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 31 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class a__ ( _SCREAMING_SNAKE_CASE ):
lowercase_ = ... | 77 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_torch
def lowerCAmelCase_ ( self ... | 31 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 202 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "M-CLIP"
def __init__( self : Tuple , _lowerCAmelCase : List[st... | 31 | 0 |
'''simple docstring'''
def _A ( lowercase__ , lowercase__ ):
lowercase__ = word.split()
def justify(lowercase__ , lowercase__ , lowercase__ ) -> str:
lowercase__ = max_width - width
lowercase__ = len(__UpperCAmelCase ... | 325 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pi... | 31 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenizati... | 31 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowercase_ :
@property
def _lo... | 270 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 31 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a (metaclass=_SCREAMING_SNAKE_CASE):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['transformers', 'torch', 'note_seq']
def __init__( self , *A__ , **A__ ) -> ... | 591 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2... | 31 | 0 |
from __future__ import annotations
from collections import namedtuple
def __lowercase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float ):
UpperCamelCase_ : Any = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ... | 417 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFea... | 31 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv... | 351 |
from __future__ import annotations
from typing import TypedDict
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list[str]:... | 31 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def _lowerCAmelCase ( lowercase : int ) ->int:
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowercase__ = F'''Input value of... | 161 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str ):
SCREAMING_SNAKE_CASE_ = {}
def lowerCAmelCase_ ( self : List[str] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCAmelCase , ... | 31 | 0 |
"""simple docstring"""
import os
import string
import sys
A_ : Any =1 << 8
A_ : Optional[int] ={
'tab': ord("""\t"""),
'newline': ord("""\r"""),
'esc': 2_7,
'up': 6_5 + ARROW_KEY_FLAG,
'down': 6_6 + ARROW_KEY_FLAG,
'right': 6_7 + ARROW_KEY_FLAG,
'left': 6_8 + ARR... | 650 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
... | 31 | 0 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__SCREAMING_SNAKE_CASE : Dict = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', '... | 661 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependenc... | 77 |
def UpperCAmelCase_ ( __UpperCAmelCase : list , __UpperCAmelCase : int , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 ) -> int:
SCREAMING_SNAKE_CASE_ = right or len(__UpperCAmelCase ) - 1
if left > right:
... | 31 | 0 |
import os
def lowerCAmelCase ( UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = len(grid[0] )
__SCREAMING_SNAKE_CASE: List[str] = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE: ... | 202 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 31 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def _A ( lowercase__ = "." ):
for dir_path, dir_names, filenames in os.walk(__UpperCAmelCase ):
lowercase__ = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
fo... | 325 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase_ ( ) -> Generator[int, None, None]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 2
while True:
SCREAMING_SNAKE_CASE_ = factor_map.pop(__Uppe... | 31 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeni... | 30 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 0 |
'''simple docstring'''
A_ = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD,... | 270 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( se... | 31 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.ut... | 591 |
def UpperCAmelCase_ ( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowerCamelCase__ : List[Any] = generate_large_matrix()
lowerCamelCase__ : List[Any] = (
[[4, 3, 2, -1], [3,... | 31 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate... | 417 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Optional[int] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 31 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xlnet-large-cased': ... | 351 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCamelCase_ :
'''simple docstring'''
@property
def lowerCAmelCase_ ... | 31 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
... | 161 |
import operator as op
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNA... | 31 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str = "The quick brown fox jumps over the lazy dog" , )-> bool:
_lowerCamelCase = set()
# Replace all the whitespace in our sentence
_lowerCamelCase = input_str.replace(' ' , '' ... | 650 |
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE_ = ... | 31 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : List[str] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'... | 661 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 31 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def _UpperCamelCase ( UpperCamelCase = 200_0000 ) -> int:
"""simple docstring"""
__UpperCAmelCase : int = [0]
__UpperCAmelCase : str ... | 77 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_torch
def lowerCAmelCase_ ( self ... | 31 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a ( _SCREAMIN... | 202 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "M-CLIP"
def __init__( self : Tuple , _lowerCAmelCase : List[st... | 31 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 325 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pi... | 31 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
c... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenizati... | 31 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
A_ = logging.get_logger(__name__)
class lowercase_ ( _SCREAMING_SNAKE_CASE ):
def __init__( self : str , ... | 270 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 31 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengt... | 591 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2... | 31 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@re... | 417 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFea... | 31 | 0 |
def a_ (__A , __A , __A , __A ) -> str:
"""simple docstring"""
if height >= 1:
move_tower(height - 1 , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
move_disk(__UpperCAmelCase , __Up... | 351 |
from __future__ import annotations
from typing import TypedDict
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list[str]:... | 31 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 161 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str ):
SCREAMING_SNAKE_CASE_ = {}
def lowerCAmelCase_ ( self : List[str] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCAmelCase , ... | 31 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Optional[int] =logging.get_logger... | 650 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Tuple = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
... | 31 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .toke... | 661 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 0 |
"""simple docstring"""
class a__ :
def __init__( self : str):
"""simple docstring"""
__UpperCAmelCase : Tuple = {}
def a_ ( self : List[str]):
"""simple docstring"""
print(self.vertex)
for i in self.vertex:
... | 77 |
def UpperCAmelCase_ ( __UpperCAmelCase : list , __UpperCAmelCase : int , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 ) -> int:
SCREAMING_SNAKE_CASE_ = right or len(__UpperCAmelCase ) - 1
if left > right:
... | 31 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCAmelCase ( UpperCamelCase__ : jnp.ndarray , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 , UpperCamelCase__ : float = 1 , UpperCamelCase__ : float = 1.0E4 , UpperCam... | 202 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 31 | 0 |
'''simple docstring'''
import string
from math import logaa
def _A ( lowercase__ , lowercase__ ):
lowercase__ = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" )
lowercase__ = d... | 325 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase_ ( ) -> Generator[int, None, None]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 2
while True:
SCREAMING_SNAKE_CASE_ = factor_map.pop(__Uppe... | 31 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a:
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=64 ,_SCREAMING_SNAKE_CASE=None ... | 30 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extract... | 270 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( se... | 31 | 0 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Optional[int]:
... | 591 |
def UpperCAmelCase_ ( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowerCamelCase__ : List[Any] = generate_large_matrix()
lowerCamelCase__ : List[Any] = (
[[4, 3, 2, -1], [3,... | 31 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def A__ ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if not is_accelerate_available():
re... | 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 32 | 1 |
import heapq
import sys
import numpy as np
UpperCAmelCase_ = tuple[int, int]
class __UpperCamelCase :
def __init__( self ):
_UpperCAmelCase = []
_UpperCAmelCase = set()
def UpperCamelCase( self ):
if not se... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://hug... | 32 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 32 |
def A__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase = str(bin(SCREAMING_SNAKE_C... | 32 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase_ = TypeVar("T")
UpperCAmelCase_ = Union[List[T], Tuple[T, ...]]
UpperCAmelCase_ = Union[T, List[T], Dict[str, T]]
UpperCAmelCase_ = Union[str, bytes, os.PathLike] | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/t... | 32 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( A__ ):
__A : List[str] = (EulerDiscreteScheduler,)
__A : Optional[int] = 10
de... | 32 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mult... | 32 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"bert-base-uncased": "https://huggingface.co... | 32 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCR... | 32 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import ... | 32 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""language-modeling""" , metadata={"""include_i... | 32 | 1 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow ... | 32 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import loggin... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https:/... | 32 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''12... | 32 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A__ ( SCREAMING_SNAKE_CASE_ : dict ) -> tuple:
... | 32 |
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , SCREAMING_SNAKE_CASE_ , (alpha * (np.exp(SCREAMING_SNAKE_CASE_ ) - 1)) )
i... | 32 | 1 |
from __future__ import annotations
from math import pow, sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance, impedan... | 32 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W"... | 32 | 1 |
def A__ ( SCREAMING_SNAKE_CASE_ : list ) -> list:
"""simple docstring"""
_UpperCAmelCase = len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase ... | 32 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDI... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://hug... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = OrderedDict(
[
... | 32 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 32 |
import baseaa
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A__ ( SCREAMING_SNAKE_CASE_ : bytes ) -> str:
"""simple docstring"""
... | 32 | 1 |
import argparse
import os
import re
UpperCAmelCase_ = "src/diffusers"
# Pattern that looks at the indentation in a line.
UpperCAmelCase_ = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_ = re.compile(r"^\s*\"([^\"]+)\":")
# Patter... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 32 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.m... | 32 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torc... | 32 | 1 |
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError('''only integers accepted as input''' )
else:
_UpperCAmelCase = str(abs(S... | 32 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = [0 for i in range(n + 1 )]
_UpperCAmelCase = 1
_UpperCAmelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class __UpperCamelCase ( A__ ):
__A : Any ... | 32 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase_ = logging.get_logger(__name__)
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
... | 32 | 1 |
from numpy import exp, pi, sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2... | 32 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( A__ ):
__A : Dict ... | 32 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __UpperCamelCase ( A__ ):
__A : str = field(default="""automatic-speech-recogniti... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
... | 32 | 1 |
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